Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity.
There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you’ll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name).
Our thanks to Manuel Spezzani, Indyco Technical Leader, and Edward William Gnudi, Indyco’s Chief of Customer Happiness, for the guest post below about using Indyco alongside Apache Impala.
In this post, you will learn how to automatically design a complete data warehouse solution on top of Impala using Indyco, a tool for designing, exploring, and understand your business model (recently named Cloudera Certificated Partner for the Impala platform).
Learn how improve Apache HBase usability by creating a custom formatter for viewing binary data types in the HBase shell.
Cloudera customers are looking to store complex data types in Apache HBase to provide fast retrieval of complex information such as banking transactions, web analytics records, and related metadata associated with those records. Serialization formats such as Apache Avro, Thrift, and Protocol Buffers greatly assist in meeting this goal,
Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running queries.
In this blog post, you will learn an approach for continuous loading of data into Impala via HDFS,
Combining CDH with a business execution engine can serve as a solid foundation for complex event processing on big data.
Event processing involves tracking and analyzing streams of data from events to support better insight and decision making. With the recent explosion in data volume and diversity of data sources, this goal can be quite challenging for architects to achieve.
Complex event processing (CEP) is a type of event processing that combines data from multiple sources to identify patterns and complex relationships across various events.